Word Sense Disambiguation and Human Intuition for Semantic Classification on Homonyms
نویسندگان
چکیده
This paper reports a psycholinguistic research for the human intuition on the sense classification. The goal of this research is to find a computational model that fits best with our experiments on human intuition. In this regard, we compare three different computational models; the Boolean model, the probabilistic model, and the probabilistic inference model. We first measured the values of each models found in the semantically annotated Sejong corpus. Then the experimental result was compared with the values in the initial measurements. Kappa statistics supports that this agreement experiment is homogeneously coincidental. The Pearson correlation coefficient test shows that the Boolean model is strongly correlated with the human intuition.
منابع مشابه
A Korean Homonym Disambiguation System Based on Statistical Model Using Weights
A homonym could be disambiguated by another words in the context as nouns, predicates used with the homonym. This paper using semantic information (co-occurrence data) obtained from definitions of part of speech (POS) tagged UMRD-S 1 ). In this research, we have analyzed the result of an experiment on a homonym disambiguation system based on statistical model, to which Bayes' theorem is applied...
متن کاملWord Type Effects on L2 Word Retrieval and Learning: Homonym versus Synonym Vocabulary Instruction
The purpose of this study was twofold: (a) to assess the retention of two word types (synonyms and homonyms) in the short term memory, and (b) to investigate the effect of these word types on word learning by asking learners to learn their Persian meanings. A total of 73 Iranian language learners studying English translation participated in the study. For the first purpose, 36 freshmen from an ...
متن کاملWord Sense Disambiguation for Exploiting Hierarchical Thesauri in Text Classification
The introduction of hierarchical thesauri (HT) that contain significant semantic information, has led researchers to investigate their potential for improving performance of the text classification task, extending the traditional “bag of words” representation, incorporating syntactic and semantic relationships among words. In this paper we address this problem by proposing a Word Sense Disambig...
متن کاملLatent Semantic Word Sense Induction and Disambiguation
In this paper, we present a unified model for the automatic induction of word senses from text, and the subsequent disambiguation of particular word instances using the automatically extracted sense inventory. The induction step and the disambiguation step are based on the same principle: words and contexts are mapped to a limited number of topical dimensions in a latent semantic word space. Th...
متن کاملAmbiguity-awaredocument Similarity
In recent years, great advances have been made in the speed, accuracy, and coverage of automatic word sense disambiguator systems that, given a word appearing in a certain context, can identify the sense of that word. In this paper we consider the problem of deciding whether same words contained in different documents are related to the same meaning or are homonyms. Our goal is to improve the e...
متن کامل